Is modelling in a post-Covid recovery overused or the essential strategic weapon?
“Modelling” seems like the new “Digital” in today’s marketing landscape, likely to become a differentiator for brands and agencies, before it becomes ubiquitous.
- As we recover from Covid, econometric modelling becomes an essential item for growth, in defending and growing media investment and your bottom line.
- Brands who were doing this pre-Covid will have an effectiveness advantage, while brands who have not, face a steep learning curve.
- Econometric modelling can be useful in both the short and long term i.e. for campaigns and quarters, but also for wider strategic direction. And with collection of accurate and timely data, can be a strategic compass for accounting for, and understanding the impact of, major disruptions, like a pandemic.
- Businesses who have already been exploring and implementing modelling pre-Covid are best placed to capitalise on this trend, as it’s traditionally an expensive exercise.
- Attribution modelling and econometric modelling are related to short and long term brand building, respectively. Attribution is easier and more focused on ROI, so brands tend to over-invest in it, but the great awakening will be for brands using a hybrid model that helps inform short and long term.
“Modelling” seems like the new “Digital” in today’s marketing landscape; a term bandied around with little understanding. Similarly to “digital”, it’ll likely become a differentiator for brands and agencies, before it becomes ubiquitous. This article however, dives into the scope of the problem, and the resultant opportunity for brands.
How many organisations refer to buying media as investments, outside of the marketing department? My belief is very few, as it’s commonly seen as a cost of doing business, rather than an investment for growth.
When asked about the role of modelling in a post-Covid world, Nina Bibby, CMO at UK telco O2, explains in the article how econometric modelling is the only way for them to truly understand on a large scale, the drivers of growth, what contributes to a sale, and how it helps accelerate their media investment profitability.
The O2 use case is a key distinction that (usually) separates brands who use modelling from those that don’t.
Modelling, econometrics and otherwise, has been used for many decades, and often shrouded in secrecy and complexities in a black box. Only in relatively recent times is it being increasingly adopted by the marketing world.
There is now enough data from businesses, campaigns, and from the wider competitive environment, to use data as a highly effective strategic asset through modelling.
And as econometrics was originally applied to mostly the finance industry, informing investment decisions, the trend is emerging for sophisticated marketers who “wargame” campaign scenarios to predict things like sales and brand uplift with a good degree of certainty, managing an “investment” rather than simply a “budget”.
This trend is likely to become a necessity as businesses enter a post-Covid world much leaner, with marketing budgets to match.
Trends like zero-based budgeting will only exacerbate the crunch, meaning that every dollar of marketing and media spend will need to be proving its effectiveness over both the short and long term.
Modelling tends to drift towards the short term at the moment, looking at campaign level issues like multi-channel attribution for sales.
The data is substantially easier to gather and model, and doesn’t rely on probability. While this is certainly useful, the example from Adidas is timely - breaking every rule in Binet & Fields The Long and the Short of It, focusing too much on these short term considerations can skew how you evaluate your media, and leads to traps like collinearity.
Channels like SEM are rated much higher in their contribution to a sale than they should be; the purchase intent from the consumer is already there, but they’ve happened to click on your ad while trying to buy the product.
Brands who are serious about modelling, and want to get the most out of it, need to be looking wider, and longer term. Blending media data with econometric data is the logical next step.
Media mix modelling gets part of the way there, but tends to be static, expensive and takes a long time, though advances in cloud processing of data are making this easier, faster, and much more affordable.
And when you get it right, you’ll have the fuel to not only interrogate creative briefs and media plans - but P&Ls, with the insights to land you a seat at the table, defending and growing budgets, and proving your marketing investment’s contribution to your company’s bottom line.
The marketing and publishing worlds continue to watch with anticipation and unease as the rules of digital marketing are overturned via the recent Apple iOS changes and the impending cookie crumble. As the demand for greater privacy and transparency regarding access and use of personal data grows, after years of normalising tracking consumer behaviour online via apps and the web, the tide is turning. Consumers are now more informed and able to make the choice as to whether they accept these terms, whether the value exchange for use of their data is worth it, and the resounding answer appears to be no. So where does that leave the world of audience targeting?
The data doesn’t lie: women are feeling confident and empowered when it comes to purchasing cars, but according to the latest research, the automotive marketing industry still has a long way to go to catch up.
Are Media has dug into the data from its inaugural HERpulse Auto survey to reveal that although the majority of women are the key decision-maker when it comes to buying a car for the family, many still feel patronised and unrepresented throughout the marketing and sales cycle.